Clinical Outcomes and Complications Following Surgical Management of Traumatic Posterior Sternoclavicular Joint Dislocations
Bibliographic record
Abstract
BACKGROUND: Traumatic posterior sternoclavicular joint dislocations are rare orthopaedic emergencies. Treatment typically consists of closed reduction, with surgical management reserved for unstable cases. Because of the low prevalence of this condition, limited clinical evidence exists for a superior surgical stabilization technique. METHODS: A systematic review of the literature following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was performed. MEDLINE and Embase databases were searched using a comprehensive search strategy. A descriptive and critical analysis of the results was performed. RESULTS: Forty relevant studies (108 cases) were identified. Favorable subjective and objective outcomes were reported for all 5 categories of stabilization described. The overall complication rate was 16%, including 4 cases of recurrent instability. Ligament reconstruction using tendon graft had the lowest recurrent instability and complication rates, and open reduction and internal fixation techniques required a second operation for implant removal in 80% of cases. CONCLUSIONS: A comprehensive review of the surgical management of traumatic posterior sternoclavicular joint dislocations is presented. Results suggest favorable outcomes for all of the methods of stabilization, with a modest complication rate. The trends observed have helped to guide the development of clinical care recommendations that aid in treatment decision-making for these injuries. LEVEL OF EVIDENCE: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".